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iperov
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223
models/Model_AVATAR/Model.py
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models/Model_AVATAR/Model.py
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from models import ModelBase
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from models import TrainingDataType
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import numpy as np
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import cv2
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from nnlib import tf_dssim
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from nnlib import conv
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from nnlib import upscale
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class Model(ModelBase):
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encoder64H5 = 'encoder64.h5'
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decoder64_srcH5 = 'decoder64_src.h5'
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decoder64_dstH5 = 'decoder64_dst.h5'
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encoder128H5 = 'encoder128.h5'
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decoder128_srcH5 = 'decoder128_src.h5'
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#override
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def onInitialize(self, **in_options):
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tf = self.tf
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keras = self.keras
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K = keras.backend
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self.set_vram_batch_requirements( {4:8,5:16,6:20,7:24,8:32,9:48} )
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self.encoder64, self.decoder64_src, self.decoder64_dst, self.encoder128, self.decoder128_src = self.BuildAE()
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img_shape64 = (64,64,1)
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img_shape128 = (256,256,3)
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if not self.is_first_run():
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self.encoder64.load_weights (self.get_strpath_storage_for_file(self.encoder64H5))
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self.decoder64_src.load_weights (self.get_strpath_storage_for_file(self.decoder64_srcH5))
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self.decoder64_dst.load_weights (self.get_strpath_storage_for_file(self.decoder64_dstH5))
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self.encoder128.load_weights (self.get_strpath_storage_for_file(self.encoder128H5))
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self.decoder128_src.load_weights (self.get_strpath_storage_for_file(self.decoder128_srcH5))
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if self.is_training_mode:
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self.encoder64, self.decoder64_src, self.decoder64_dst, self.encoder128, self.decoder128_src = self.to_multi_gpu_model_if_possible ( [self.encoder64, self.decoder64_src, self.decoder64_dst, self.encoder128, self.decoder128_src] )
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input_src_64 = keras.layers.Input(img_shape64)
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input_src_target64 = keras.layers.Input(img_shape64)
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input_src_target128 = keras.layers.Input(img_shape128)
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input_dst_64 = keras.layers.Input(img_shape64)
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input_dst_target64 = keras.layers.Input(img_shape64)
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src_code64 = self.encoder64(input_src_64)
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dst_code64 = self.encoder64(input_dst_64)
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rec_src64 = self.decoder64_src(src_code64)
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rec_dst64 = self.decoder64_dst(dst_code64)
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src64_loss = tf_dssim(tf, input_src_target64, rec_src64)
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dst64_loss = tf_dssim(tf, input_dst_target64, rec_dst64)
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total64_loss = src64_loss + dst64_loss
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self.ed64_train = K.function ([input_src_64, input_src_target64, input_dst_64, input_dst_target64],[K.mean(total64_loss)],
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self.keras.optimizers.Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(total64_loss, self.encoder64.trainable_weights + self.decoder64_src.trainable_weights + self.decoder64_dst.trainable_weights)
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)
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src_code128 = self.encoder128(input_src_64)
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rec_src128 = self.decoder128_src(src_code128)
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src128_loss = tf_dssim(tf, input_src_target128, rec_src128)
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self.ed128_train = K.function ([input_src_64, input_src_target128],[K.mean(src128_loss)],
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self.keras.optimizers.Adam(lr=5e-5, beta_1=0.5, beta_2=0.999).get_updates(src128_loss, self.encoder128.trainable_weights + self.decoder128_src.trainable_weights)
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)
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src_code128 = self.encoder128(rec_src64)
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rec_src128 = self.decoder128_src(src_code128)
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self.src128_view = K.function ([input_src_64], [rec_src128])
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if self.is_training_mode:
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from models import TrainingDataGenerator
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f = TrainingDataGenerator.SampleTypeFlags
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self.set_training_data_generators ([
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TrainingDataGenerator(TrainingDataType.FACE, self.training_data_src_path, debug=self.is_debug(), batch_size=self.batch_size, output_sample_types=[
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[f.WARPED_TRANSFORMED | f.HALF_FACE | f.MODE_G, 64],
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[f.TRANSFORMED | f.HALF_FACE | f.MODE_G, 64],
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[f.TRANSFORMED | f.FULL_FACE | f.MODE_BGR, 256],
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[f.SOURCE | f.HALF_FACE | f.MODE_G, 64],
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[f.SOURCE | f.HALF_FACE | f.MODE_GGG, 256] ] ),
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TrainingDataGenerator(TrainingDataType.FACE, self.training_data_dst_path, debug=self.is_debug(), batch_size=self.batch_size, output_sample_types=[
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[f.WARPED_TRANSFORMED | f.HALF_FACE | f.MODE_G, 64],
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[f.TRANSFORMED | f.HALF_FACE | f.MODE_G, 64],
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[f.SOURCE | f.HALF_FACE | f.MODE_G, 64],
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[f.SOURCE | f.HALF_FACE | f.MODE_GGG, 256] ] )
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])
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#override
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def onSave(self):
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self.save_weights_safe( [[self.encoder64, self.get_strpath_storage_for_file(self.encoder64H5)],
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[self.decoder64_src, self.get_strpath_storage_for_file(self.decoder64_srcH5)],
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[self.decoder64_dst, self.get_strpath_storage_for_file(self.decoder64_dstH5)],
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[self.encoder128, self.get_strpath_storage_for_file(self.encoder128H5)],
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[self.decoder128_src, self.get_strpath_storage_for_file(self.decoder128_srcH5)],
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] )
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#override
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def onTrainOneEpoch(self, sample):
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warped_src64, target_src64, target_src128, target_src_source64_G, target_src_source128_GGG = sample[0]
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warped_dst64, target_dst64, target_dst_source64_G, target_dst_source128_GGG = sample[1]
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loss64, = self.ed64_train ([warped_src64, target_src64, warped_dst64, target_dst64])
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loss256, = self.ed128_train ([warped_src64, target_src128])
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return ( ('loss64', loss64), ('loss256', loss256) )
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#override
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def onGetPreview(self, sample):
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n_samples = 4
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test_B = sample[1][2][0:n_samples]
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test_B128 = sample[1][3][0:n_samples]
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BB, = self.src128_view ([test_B])
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st = []
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for i in range(n_samples // 2):
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st.append ( np.concatenate ( (
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test_B128[i*2+0], BB[i*2+0], test_B128[i*2+1], BB[i*2+1],
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), axis=1) )
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return [ ('AVATAR', np.concatenate ( st, axis=0 ) ) ]
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def predictor_func (self, img):
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x, = self.src128_view ([ np.expand_dims(img, 0) ])[0]
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return x
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#override
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def get_converter(self, **in_options):
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return ConverterAvatar(self.predictor_func, predictor_input_size=64, output_size=256, **in_options)
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def BuildAE(self):
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keras, K = self.keras, self.keras.backend
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def Encoder(_input):
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x = keras.layers.convolutional.Conv2D(90, kernel_size=5, strides=1, padding='same')(_input)
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x = keras.layers.convolutional.Conv2D(90, kernel_size=5, strides=1, padding='same')(x)
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x = keras.layers.MaxPooling2D(pool_size=(3, 3), strides=2, padding='same')(x)
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x = keras.layers.convolutional.Conv2D(180, kernel_size=3, strides=1, padding='same')(x)
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x = keras.layers.convolutional.Conv2D(180, kernel_size=3, strides=1, padding='same')(x)
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x = keras.layers.MaxPooling2D(pool_size=(3, 3), strides=2, padding='same')(x)
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x = keras.layers.convolutional.Conv2D(360, kernel_size=3, strides=1, padding='same')(x)
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x = keras.layers.convolutional.Conv2D(360, kernel_size=3, strides=1, padding='same')(x)
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x = keras.layers.MaxPooling2D(pool_size=(3, 3), strides=2, padding='same')(x)
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x = keras.layers.Dense (1024)(x)
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x = keras.layers.advanced_activations.LeakyReLU(0.1)(x)
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x = keras.layers.Dropout(0.5)(x)
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x = keras.layers.Dense (1024)(x)
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x = keras.layers.advanced_activations.LeakyReLU(0.1)(x)
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x = keras.layers.Dropout(0.5)(x)
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x = keras.layers.Flatten()(x)
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x = keras.layers.Dense (64)(x)
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return keras.models.Model (_input, x)
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encoder128 = Encoder( keras.layers.Input ( (64, 64, 1) ) )
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encoder64 = Encoder( keras.layers.Input ( (64, 64, 1) ) )
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def decoder128_3(encoder):
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decoder_input = keras.layers.Input ( K.int_shape(encoder.outputs[0])[1:] )
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x = decoder_input
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x = self.keras.layers.Dense(16 * 16 * 720)(x)
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x = keras.layers.Reshape ( (16, 16, 720) )(x)
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x = upscale(keras, x, 720)
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x = upscale(keras, x, 360)
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x = upscale(keras, x, 180)
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x = upscale(keras, x, 90)
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x = keras.layers.convolutional.Conv2D(3, kernel_size=5, padding='same', activation='sigmoid')(x)
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return keras.models.Model(decoder_input, x)
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def decoder64_1(encoder):
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decoder_input = keras.layers.Input ( K.int_shape(encoder.outputs[0])[1:] )
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x = decoder_input
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x = self.keras.layers.Dense(8 * 8 * 720)(x)
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x = keras.layers.Reshape ( (8,8,720) )(x)
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x = upscale(keras, x, 360)
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x = upscale(keras, x, 180)
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x = upscale(keras, x, 90)
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x = keras.layers.convolutional.Conv2D(1, kernel_size=5, padding='same', activation='sigmoid')(x)
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return keras.models.Model(decoder_input, x)
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return encoder64, decoder64_1(encoder64), decoder64_1(encoder64), encoder128, decoder128_3(encoder128)
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from models import ConverterBase
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from facelib import FaceType
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from facelib import LandmarksProcessor
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class ConverterAvatar(ConverterBase):
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#override
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def __init__(self, predictor,
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predictor_input_size=0,
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output_size=0,
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**in_options):
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super().__init__(predictor)
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self.predictor_input_size = predictor_input_size
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self.output_size = output_size
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#override
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def get_mode(self):
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return ConverterBase.MODE_IMAGE
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#override
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def dummy_predict(self):
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self.predictor ( np.zeros ( (self.predictor_input_size, self.predictor_input_size,1), dtype=np.float32) )
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#override
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def convert_image (self, img_bgr, img_face_landmarks, debug):
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img_size = img_bgr.shape[1], img_bgr.shape[0]
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face_mat = LandmarksProcessor.get_transform_mat (img_face_landmarks, self.predictor_input_size, face_type=FaceType.HALF )
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predictor_input_bgr = cv2.warpAffine( img_bgr, face_mat, (self.predictor_input_size, self.predictor_input_size), flags=cv2.INTER_LANCZOS4 )
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predictor_input_g = np.expand_dims(cv2.cvtColor(predictor_input_bgr, cv2.COLOR_BGR2GRAY),-1)
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predicted_bgr = self.predictor ( predictor_input_g )
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output = cv2.resize ( predicted_bgr, (self.output_size, self.output_size), cv2.INTER_LANCZOS4 )
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if debug:
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return (img_bgr,output,)
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return output
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